Show simple item record

dc.contributor.authorRoh, S.
dc.contributor.authorJun, M.
dc.contributor.authorSzunyogh, I.
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2015-12-20T10:59:10Z
dc.date.available2015-12-20T10:59:10Z
dc.date.issued2015-12-03
dc.identifier.citationMultivariate localization methods for ensemble Kalman filtering 2015, 22 (6):723 Nonlinear Processes in Geophysics
dc.identifier.issn1607-7946
dc.identifier.doi10.5194/npg-22-723-2015
dc.identifier.urihttp://hdl.handle.net/10754/584235
dc.description.abstractIn ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
dc.language.isoen
dc.publisherCopernicus GmbH
dc.relation.urlhttp://www.nonlin-processes-geophys.net/22/723/2015/
dc.rightsArchived with thanks to Nonlinear Processes in Geophysics. This work is distributed under the Creative Commons Attribution 3.0 License.
dc.titleMultivariate localization methods for ensemble Kalman filtering
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalNonlinear Processes in Geophysics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX 77843-3143, USA
dc.contributor.institutionDepartment of Atmospheric Sciences, Texas A&M University, College Station, TX 77843-3148, USA
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personGenton, Marc G.
refterms.dateFOA2018-06-14T09:29:05Z


Files in this item

Thumbnail
Name:
npg-22-723-2015.pdf
Size:
912.6Kb
Format:
PDF
Description:
Main article

This item appears in the following Collection(s)

Show simple item record